Machining, the Better – A Look at Machine Learning-Based Volatility Using the SVR-GARCH for Frontier Market Equities

Financial market volatility is a critical factor influencing investment decisions, risk management, and economic policy. Traditional statistical models often struggle to capture the complex, nonlinear relationships in financial data, leading to suboptimal volatility forecasting. Machine learning (ML) techniques presently offer a promising alternative by leveraging vast amounts of historical data to identify hidden patterns and improve predictive accuracy. We explore the use of a hybrid Supervised Vector Machine (SVM) -GARCH algorithm against the various variant of GARCH to study the heteroscedasticity of the equity returns drawn from the Ghana Stock Exchange (GSE) as typically representing frontier markets in sub-Saharan African markets. We compared the effectiveness of these hybrid models to the classical GARCH and highlighted the advantages of data-driven ML models in adapting to dynamic market conditions. Our findings demonstrate that ML-based models can enhance forecasting performance, reduce estimation errors, and provide deeper insights into market behavior, making them valuable tools for both investors and policymakers in frontier and developed markets alike .